{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KEGGutils Tutorial 2 : `KEGGgraph`s, `KEGGlinkgraph`s and `KEGGchain`s\n",
    "## .... redoing Tutorial 1 with different tools\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../img/logo_cut.png\" alt=\"Drawing\" style=\"width: 300px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First things first\n",
    "Let's make sure we imported `KEGGutils` ( and that we can find it in scope if we did not install it )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,inspect\n",
    "currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
    "parentdir = os.path.dirname(currentdir)\n",
    "sys.path.insert(0,parentdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import KEGGutils as kg\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and that that we have the last available version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.3.2'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kg.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's remove the cached files to use the freshest available data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> deleting the following files from C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\n",
      "C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\\disease-list\n",
      "C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\\ds-H00773-description\n",
      "C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\\enzyme-hsa-link\n",
      "C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\\hsa-disease-link\n",
      "C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\\reaction-enzyme-link\n"
     ]
    }
   ],
   "source": [
    "kg.delete_cached_files()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introducing `KEGGgraph`, `KEGGlinkgraph` and `KEGGchain`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous tutorial we've seen how we can use `KEGGutil`'s function set to create and manage vanilla `networkx` graphs with KEGG data, in this tutorial we're gonna use some special graph-like classes designed specifically for KEGG data: `KEGGgraph`, `KEGGlinkgraph` and `KEGGchain`.\n",
    "\n",
    "`KEGGlinkgraph` and `KEGGchain` (as `KEGGpathway` which we'll se in the next tutorial ) are children of `KEGGgraph` which inherits from `netowrkx.Graph`: that means that you'll be able to use any `networkx` method on these objects and are not limited to `KEGGutils`'s tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base class from KEGGutils NetworkX compatible Graphs:\n",
      "    Directly inherits from networkx.Graph()\n",
      "\n",
      "    Methods\n",
      "    -------\n",
      "    list_by_nodetype(nodetype)\n",
      "        returns a dict of nodes for the given nodetype in the form {key: nodetype}\n",
      "    \n",
      "    connected_components()\n",
      "        returns a list of connected components\n",
      "    \n",
      "    linked_nodes(node)\n",
      "        returns a dict of nodes in the graph connected to a givne node in the form {key: nodetype}\n",
      "\n",
      "     graph_measures()\n",
      "        returns a list of graph characteristics\n",
      "    \n",
      "    shortest_path(source_node, target_node):\n",
      "        computes the shortest path between two nodes\n",
      "    \n",
      "    compose(othergraph, inplace = False)\n",
      "        returns graph composition between self and one or more other graphs\n",
      "    \n",
      "    neighbor_graph(nodedict, keep_isolated_nodes, inplace = False)\n",
      "        returns graph of internal neighbors to a given dict of nodes\n",
      "\n",
      "    get_unique_nodetypes()\n",
      "        returns unique nodetypes in graph\n",
      "\n",
      "    draw(layout = None)\n",
      "        plots the graph\n",
      "    \n",
      "    prune_isolated_nodes(inplace = False)\n",
      "        removes isolated nodes\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(kg.KEGGgraph.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this quick summary we can see the main methods that are common to all `KEGGgraph`s: we can see some of the tools that we used before like `neighbor_graph` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neighbor Graph\n",
      "        \n",
      "        Parameters\n",
      "        ----------\n",
      "        nodelist : list\n",
      "            contains the nodes we wish to calculate the neighbor graph respect to\n",
      "        keep_isolated_nodes : bool, optional\n",
      "            Isolated nodes can be automatically removed selecting False (the default is True)\n",
      "        inplace : bool, optional\n",
      "            Substitutes the original graph with the neighbor graph (the default is False, which [default_description])\n",
      "        \n",
      "        Returns\n",
      "        -------\n",
      "        graph\n",
      "            neighbor graph\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "print(kg.KEGGgraph.neighbor_graph.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and some tools we have not used yet like `compose` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compose\n",
      "        Calculates composition between two or mre graphs\n",
      "        \n",
      "        Parameters\n",
      "        ----------\n",
      "        othergraph : KEGGgraph\n",
      "            Can be a single KEGGgraph, a list of graphs or a tuple of graphs, contains the other graphs you want to calculate composition respect to\n",
      "        inplace : bool, optional\n",
      "            If True the original graph calling the method is substituted by the composition (the default is False)\n",
      "        \n",
      "        Returns\n",
      "        -------\n",
      "        KEGGgraph\n",
      "            composed graph\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "print(kg.KEGGgraph.compose.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if these seem to few that's because there are other methods hidden in the depths of `KEGGgraph` children classes, like the `KEGGlinkgraph` method `projected_graph`, that we used before "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Projects the link graph on a subset of nodes\n",
      "        \n",
      "        Parameters\n",
      "        ----------\n",
      "        nodelist : list, optional\n",
      "            list of nodes you wish to project onto, if None automatically selects source nodes\n",
      "        name : str, optional\n",
      "            name of the graph\n",
      "        \n",
      "        \n",
      "        Returns\n",
      "        -------\n",
      "        graph\n",
      "            projection graph\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "print(kg.KEGGlinkgraph.projected_graph.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and many other methods are scheduled for future developement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using `KEGGlinkgraph`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start our tutorial with a class that makes life easier whe dealing with KEGG LINK data: `KEGGlinkgraph`\n",
    "\n",
    "`KEGGlinkgraph`s are specifically meant to create biparite graphs between couples of KEGG databases automating the process of data retrieval and graph population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KEGGlinkgraph class inherits from KEGGgraph\n",
      "    Can be used to build bipartite graphs from KEGG API LINK functionality \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    source_db : str\n",
      "        Source Database\n",
      "    target_db : str\n",
      "        Target Database\n",
      "\n",
      "    Properties\n",
      "    ----------\n",
      "    source_nodes : dict\n",
      "        dict of source nodes {node: nodetype}\n",
      "    target_nodes : dict\n",
      "        dict of target nodes {node: nodetype}\n",
      "    source_liked_db : list\n",
      "        list of databases connected to source in KEGG\n",
      "    target_linked_db : list\n",
      "        list of databases connected to target in KEGG\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(kg.KEGGlinkgraph.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "creating a bipartite graph between two databases is easy and only involves calling the constructor with the parameters `source_db` and `target_db`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:> Downloading hsa-disease-link from KEGG at http://rest.kegg.jp/link/hsa/disease\n",
      "INFO:root:succesfully downloaded hsa-disease-link\n",
      "INFO:root:> Downloading enzyme-hsa-link from KEGG at http://rest.kegg.jp/link/enzyme/hsa\n",
      "INFO:root:succesfully downloaded enzyme-hsa-link\n",
      "INFO:root:> Downloading reaction-enzyme-link from KEGG at http://rest.kegg.jp/link/reaction/enzyme\n",
      "INFO:root:succesfully downloaded reaction-enzyme-link\n"
     ]
    }
   ],
   "source": [
    "dis_gene = kg.KEGGlinkgraph(source_db = \"disease\", target_db = \"hsa\")\n",
    "gene_enzyme = kg.KEGGlinkgraph(source_db = \"hsa\", target_db =\"enzyme\")\n",
    "enzyme_reaction = kg.KEGGlinkgraph(source_db = \"enzyme\", target_db = \"reaction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The KEGGlinkgraph disease_to_hsa is a bipartite graph that links disease database to hsa database \n"
     ]
    }
   ],
   "source": [
    "print(\"The KEGGlinkgraph {} is a bipartite graph that links {} database to {} database \".format(dis_gene.name,\n",
    "                                                                                                dis_gene.source_db,\n",
    "                                                                                                dis_gene.target_db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can also have infos on source and target databases with a simple command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Infos on hsa from KEGG:\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T01001           Homo sapiens (human) KEGG Genes Database\n",
      "hsa              Release 90.0+/04-02, Apr 19\n",
      "                 Kanehisa Laboratories\n",
      "                 38,683 entries\n",
      "\n",
      "linked db        pathway\n",
      "                 brite\n",
      "                 module\n",
      "                 ko\n",
      "                 genome\n",
      "                 enzyme\n",
      "                 network\n",
      "                 disease\n",
      "                 drug\n",
      "                 ncbi-geneid\n",
      "                 ncbi-proteinid\n",
      "                 uniprot\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dis_gene.target_infos()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and easily check which other databases are linked to the current ones in KEGG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pathway',\n",
       " 'brite',\n",
       " 'module',\n",
       " 'ko',\n",
       " 'genome',\n",
       " 'enzyme',\n",
       " 'network',\n",
       " 'disease',\n",
       " 'drug',\n",
       " 'ncbi-geneid',\n",
       " 'ncbi-proteinid',\n",
       " 'uniprot']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_gene.target_linked_db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before we wish to investigate a particular disease:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'nodetype': 'disease', 'label': 'ds:H00773'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis_gene.nodes['ds:H00773']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Infos on ds:H00773 from KEGG:\n",
      "\n",
      "NAME        Autosomal dominant mental retardation\n",
      "DESCRIPTION Mental retardation (MR) is a neurodevelopmental disorder characterized by low intelligence quotient (IQ) and deficits in adaptive behaviors. Most MR genes identified so far were either located on the X chromosome or are associated with an autosomal recessive mode of inheritance. Recently, a lot of genes associated with autosomal dominant mental retardation have been identified.\n",
      "CATEGORY    Mental and behavioural disorder\n"
     ]
    }
   ],
   "source": [
    "kg.keggapi_get(\"ds:H00773\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we may want now to see which nodes are liked to the chosen disease, that can be done using the `linked_nodes` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hsa:55777',\n",
       " 'hsa:81704',\n",
       " 'hsa:1013',\n",
       " 'hsa:84623',\n",
       " 'hsa:8831',\n",
       " 'hsa:2904',\n",
       " 'hsa:1859',\n",
       " 'hsa:2902',\n",
       " 'hsa:547',\n",
       " 'hsa:10369']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_genes = dis_gene.linked_nodes(\"ds:H00773\")\n",
    "ds_genes[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can have the same information in a dict-like format using the `return_dict` option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('hsa:55777', 'hsa'),\n",
       " ('hsa:81704', 'hsa'),\n",
       " ('hsa:1013', 'hsa'),\n",
       " ('hsa:84623', 'hsa'),\n",
       " ('hsa:8831', 'hsa'),\n",
       " ('hsa:2904', 'hsa'),\n",
       " ('hsa:1859', 'hsa'),\n",
       " ('hsa:2902', 'hsa'),\n",
       " ('hsa:547', 'hsa'),\n",
       " ('hsa:10369', 'hsa')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dis_gene.linked_nodes(\"ds:H00773\", return_dict = True).items())[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's narrow down our search in the `gene_enzyme` graph using the nodes  we just found and plot what we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\filippo.castelli4\\AppData\\Local\\Continuum\\anaconda3\\envs\\KEGGenv\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_gene_enzyme = gene_enzyme.neighbor_graph(ds_genes)\n",
    "ds_gene_enzyme.draw(layout = \"kamada_kawai_layout\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if this plot is too confusing we can always choose to ignore the unconnected nodes using the `prune_isolated_nodes` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_gene_enzyme.prune_isolated_nodes().draw(layout = \"circular_layout\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "note that all the `KEGGgraph` main methods are chainable, and that they won't have a permanent effect on the original graph unless the `inplace` option is set to True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's dig down in our search selecting only the enzymes we're interested in the  `enzyme_reaction` graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_enzymes = ds_gene_enzyme.list_by_nodetype(\"enzyme\")\n",
    "ds_enzyme_reaction = enzyme_reaction.neighbor_graph(ds_enzymes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_enzyme_reaction.prune_isolated_nodes().draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and finally we can project this last graph onto it's `enzyme` components, note that if we're trying a projection on a `KEGGlikgraph` without specifying a set of nodes to project onto, `KEGGutils` will assume you're projecting onto the `graph.source_nodes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_enzyme_reaction.projected_graph().draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "after all, `KEGGlinkgraph` class makes the operation a bit less clunky than in Tutorial 1 but wouldn't it be better if this could be achieved with one or two lines of code?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introducing `KEGGchain`s:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To deal with multiple interlinked databases and to facilitate data discovery, `KEGGutils` provides the `KEGGchain` class: if we have a series of sequentially linked databases we can chain them with a `KEGGchain`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " KEGGchain\n",
      "    Can be used to build chains of KEGGlinkgraphs, builds a composed graph of sequentially linked databases\n",
      "\n",
      "    --------------\n",
      "    chain : list\n",
      "        list of databases, they have to be sequentially linked in KEGG\n",
      "\n",
      "    Properties\n",
      "    ----------\n",
      "\n",
      "    chain_dbs : list\n",
      "        list of databases\n",
      "    chain : list\n",
      "        list of chained KEGGlinkgraphs\n",
      "    directed_chain : diGraph\n",
      "        directed version of the chain\n",
      "        \n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(kg.KEGGchain.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Infos on disease from KEGG:\n",
      "\n",
      "WARNING:root:> File hsa-disease-link already present in C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\n",
      "reading from chached file...\n",
      "INFO:root:Infos on hsa from KEGG:\n",
      "\n",
      "WARNING:root:> File enzyme-hsa-link already present in C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\n",
      "reading from chached file...\n",
      "INFO:root:Infos on enzyme from KEGG:\n",
      "\n",
      "WARNING:root:> File reaction-enzyme-link already present in C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\n",
      "reading from chached file...\n"
     ]
    }
   ],
   "source": [
    "mychain = kg.KEGGchain(chain = [\"disease\", \"hsa\", \"enzyme\", \"reaction\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`KEGGchain`s are basically `KEGGgraph`s on steroids: while they're intrinsically an undirected `KEGGgraph` that combines multiple `KEGGlinkgraphs`, they have, as properties, a directed version of themselves and a list of the `KEGGlinkgraph`s that originated them in the first place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chains belong to <class 'KEGGutils.KEGGgraphs.KEGGchain'>\n",
      "They have a .chain property that is a list of KEGGlinkgraphs\n",
      "and a .directed_chain that is a DiGraph\n"
     ]
    }
   ],
   "source": [
    "print(\"chains belong to {}\".format(type(mychain)))\n",
    "print(\"They have a .chain property that is a {} of {}s\".format(type(mychain.chain).__name__,\n",
    "                                                               type(mychain.chain[0]).__name__))\n",
    "print(\"and a .directed_chain that is a {}\".format(type(mychain.directed_chain).__name__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this may seem a bit confusing at first but here's an explanatory [pic](https://i.imgflip.com/2xbwd2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They have a list of the `KEGGlinkgraph`s they combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<KEGGutils.KEGGgraphs.KEGGlinkgraph at 0x29b4c45f390>,\n",
       " <KEGGutils.KEGGgraphs.KEGGlinkgraph at 0x29b4c760b38>,\n",
       " <KEGGutils.KEGGgraphs.KEGGlinkgraph at 0x29b4c760c50>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mychain.chain "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first chain element links disease to hsa,\n",
      "second chain element links hsa to enzyme,\n",
      "and third chain element links enzyme to reaction\n"
     ]
    }
   ],
   "source": [
    "print(\"The first chain element links {} to {},\".format(mychain.chain[0].source_db, mychain.chain[0].target_db))\n",
    "print(\"second chain element links {} to {},\".format(mychain.chain[1].source_db, mychain.chain[1].target_db))\n",
    "print(\"and third chain element links {} to {}\".format(mychain.chain[2].source_db, mychain.chain[2].target_db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and a directed version of the graph combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mychain.directed_chain.is_directed()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two interesting methods `KEGGchain` provides are `directed_propagation()` and `projected_graph()`\n",
    "\n",
    "with `directed_propagation` we can choose to select from the chain only the nodes that are directly descending from a particular set of nodes up in the chain: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directed propagation\n",
      "        Computes the subchain of nodes that are sequentially linked to nodelist\n",
      "        \n",
      "        Parameters\n",
      "        ----------\n",
      "        nodelist : list\n",
      "            list of nodes you wish to propagate from\n",
      "        chain_level : int, optional\n",
      "            If the nodes are not in the first level of the chain you need to specify which level they're in\n",
      "        inplace : bool, optional\n",
      "            if True propagates the changes to the original KEGGchain (the default is False])\n",
      "        return_directed : bool, optional\n",
      "            if True returns the directed chain instead of the original chain (the default is False)\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "print(mychain.directed_propagation.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with `directed_propagation` we can choose to select from the chain only the nodes that are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_chain = mychain.directed_propagation([\"ds:H00773\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop_chain.draw(layout = \"kamada_kawai_layout\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and with `projected_graph()` we can perform the same graph projection we did before in one easy step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Projected Graph\n",
      "        Calculates the projection of a given chain level onto the next \n",
      "        \n",
      "        Parameters\n",
      "        ----------\n",
      "        chain_level : str\n",
      "            name of the interested chain level, if None projection of the second to last level is made onto the last (default is None)\n",
      "\n",
      "        Returns\n",
      "        -------\n",
      "        KEGGgraph\n",
      "            projected graph\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "print(prop_chain.projected_graph.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+/fsXDL1Zs+g8diwHz57NPmawefPHrzgqL6fOHauqYt1NN7GyqopN+cazO6urq7P58+fvO2DAgHxv5ig5qv4LAGZcQXj79XDCy0ors3qpjJ8PB2bG/kXas0yh0Lv9droOHcpRzzxDly1bsGToAWzeTGbLFmzGDLoMHcpRt9+e94W3u7W4jFKVBTrVKfWhdwfhHWcNycT+7jADdyY2NIBIe3P77XQdM4bDamoa3uG7Q00NmTFjwgu5r7+eol/FJO1LqlJedhZPbzY29JLqw++4li+VyK4zaxadGxt6SfXhN3t20duKtDMKvhQzsx9D36fgpM7hnbG5DAEGA8cBD2Z3rICXx5rZ3PgSzFMbmN4sM5vU7IKLNMPYsRy8eXPT9n2bN5MZO5aDWrpM0roUfKk24kF4tRKeAx4DluboZyrh0t8f+PgF6ttloHcVjB0JPFxoSmZ2DrC+2UUWaYblyymbPZt9mtqmzx1mzaLLihWNu0z005/+dL/+/fv37dOnT79LLrnk8NraWjp37vzpq6+++pCjjjqq34ABA/osW7asDKBPnz796n8qKioGPvnkk3sefvjhx6xYsaIMYNu2bfTo0eOYlStXll1wwQU9L7300h4nnnhi70MPPbT/k08+ueeIESN6HnHEEUdfcMEFPeun/8gjj+z9qU99qk+/fv36Dhs27Ih169Zpn4+Cr6SY2fhYq5pjZueYWQ8zmxY/e8bMMmY21MwuC0M8dBrgYTXoEH+ydYq/PwKOztF9D4ebhjdQrgzwDeDuJs6aSIu45x72N2u4v0LM8HvuYf+G+vvLX/5S8fDDD+/30ksvLV68ePGiTCbjEydO3L+6ujpz8sknb3zttdcWnXzyyRvvuuuubgCLFy9etHjx4kW33HLL8qOPPvqjIUOGfPSFL3zhg0mTJu0H8Nhjj+3dt2/f6oMPPrgWYN26dWVz5sxZctttty276KKLPvmtb33r3ddff33h4sWLK5977rnKlStXlo0bN+7g2bNnL1m0aNGrAwcO3HTrrbce2Ly5Lw1q3FIizGwosK+7V5lZZ2AOsASY4O7TzSzj7nXAtMRgxwKVMAU4EuiZY8zbgNOBhcD4XJOujOOpK1C8LwOPADVFzZRIC5s/n8rs1pvF2ryZzPz5O7V63sm0adP2WrBgQecBAwb0BaipqckccMABtR07dvSRI0euAxg0aNBHM2bM2Pvj8s0vv+mmmw599tlnl5SXl/uVV175/nnnndfrlltuee8Xv/hF18svv3x7w5rPfe5zazOZDAMHDty0//77bz3hhBOqAXr37l29dOnS8rfffrvT0qVLK0444YQ+AFu3brVBgwZtbM68lwoFX+noD1SZ2cz4fznQD3gWIIZeti4wA7gfeDzPaDsAs4APgOOBC4F9coyHNbmGNrMK4FJgKFDwGqDIrrZ+fcvs8xozHne3ESNGfHD33XcvT34+ceLEAzOZcLKtrKyM2tpaC+Ncn7nwwguPvOeee97u2bPnVoBevXpt7dq1a+3vfve7vV5++eU9Hn300Tfqx1NRUeEAHTp0oFOnTttP3mYyGWpra61Dhw5+6qmnrn/88cffbIl5LiU61Vk6FgLT3X2wuw8m1MIWElqm1J9uzPJ4B/gu4fJcrgPYrXxckduDcBtfRa5pry1Qrk8QgvEJ4IfAWWY2qqGZEdkV9t67ZZ7C0pjxDB06dP0TTzyx7/Lly8sA3n333Q5LlizplK//kSNH9rz00kvfHzp06A61sq9+9aurR40a9YnzzjtvTVlZ43N78ODBH7300kt7LliwoBxgw4YNmVdeeaW80SMoYQq+EuHuU4H1ZjbTzJ4F7gOuA64zs1nA9J2v8Y0aCOsdzifk49w4tmuA1cB78fN/IJzu/C6hIrkKGB37XVcNvU4CPgvcaWbfAzCzy83sTHd/1d2Pc/ehwLeBp9xdLTulTfTvT3V5Oc16XFV5OXX9+1PdUH+DBg2qufnmm5efccYZvXv37t3v9NNP771s2bKOufpdsmRJp2nTpu37wAMPdK1v4DJ79uzOABdffPG6TZs2dfj617/+QTHl7N69e+2999771siRI4/o3bt3v0GDBvWZP39+ziPXtNEjy1IsPpPzbfJU4xqpBuihZ3hKezNv3ry3BgwYsMPN5suXU3bEERy7ZUvTr/N16oS/+SavtNYzPGfPnt352muvPWzu3Lmv7appzJs3r+uAAQN67qrxtzeq8aWYO+8Rnr1ZqGFKIXXAVIWe7C4OOYTa005jXVNbdppBVRVrWyv0brzxxoNGjhx55Lhx45Y33Lc0loJPxtP01pY15GnqKdJe3XwzK8vLm3awV15O3c03s6qly5TPuHHjVq1YsWL+WWedpdaYLUjBl3LxLQujoegnz28CRrvzUsuXSmTXqapi05gxLKuoKC78KiqoGzOGZaedVppvaUgT3c4guDMxnvq5g3C9r9ABUR2hpjdaD6iW3VX9g6bHjOGwzZvJFGrqYBZqemPGsEwPqC4NqvEJsP0tC1XAo4Rgy261Vh0/fxSoUujJ7u7663n/qad4bcgQPuzUCc8+/VleTl2nTviQIXz41FO8ptArHarxyXbxtOUFZnQj9xvY71dDFiklp53GpunTeWPFitxvYL/yytJ+A3taKfhkJzHc/r2tyyHSWrp3p/bWW3m3rcshrUOnOkVEJFUUfCIiu4heS9Q+aSGIiOwCei1R+6VrfCKSCt/85je733nnnQe31PiuvfbalRMmTFiRr7teS9R+KfhEJBUmTJiwolBQtTS9lqj90qlOEZFdQK8lar8UfCIiu4BeS9R+6bVEIlKScr2WaHek1xK1PF3jExFpp2688caDJk+e3O2Xv/ylrtO1INX4RKQklUqNrzWkrcana3wiUqrq6urqmvym9bSIy6ipL6PeLSn4RKRULVi9evU+Cr/86urqbPXq1fsAC9q6LK1J1/hEpCTV1taOWrVq1aRVq1Ydgw7y86kDFtTW1o5q64K0Jl3jExGRVNFRkIiIpIqCT0REUkXBJyIiqaLgExGRVFHwiYhIqij4REQkVRR8IiKSKgo+ERFJFQWfiIikioJPRERSRcEnIiKpouATEZFUUfCJiEiqKPhERCRVFHwiIpIqCj4REUkVBZ+IiKSKgk9ERFJFwSciIqmi4BMRkVQpa+sCiIhI05hxAPBl4FigC7AWeAWY7M7qtixbe2bu3tZlEBEpKbs6kMw4HrgBGAY4UJnoXA0Y8HtgvDsvNnd6pUbBJyLSQlojkMy4ArgDqKDw5ao6oAYY7c7EpkyrVCn4RERaQGsEUmIanYsYbFNTplXKFHwiIs3UGoEUa5Mzi5xGclpV7rzUhGFLjlp1iog0QwykYkOP2P8dZhzX8DTsx9D3KTipM9yWo481wODET0fgw2QPFfDyWDOba2YbzezU5LjN7Pn4850i52G3pOATEWmeGwinN5uiIg7fgBEPwquV8BzwGLA0q/t+hMrgTOCHwBnAvskeMtC7CsaOBB7OGvhudz8JOAUYbmZHFj0XuxkFn4hIE5l1uRdOPB9OyhRXE7sBqAJOzsC955h1XGRmtWb2spk9Y2YZM/u+mS03sxp46DTAwy67Q/zJ5wHgizk+38PhpuHZn7r76/F3HbAt/pQ0BZ+ISJP9di28UFNcTewFQvjNAp4B7uoIveuAPwBXA2fGEPoJcBSwCt75B6ASpgBHAj3zlKcWeAI4P1fHSsLtFTmZ2WXAUnd/q9AclwLdwC4iEpnZeMIpv07ADwj33v2MEBq1wJnAZ4Fu7j4FzuweuhVTE5tPCL3B8fNag439gF7AdKCDmZWxQ8Xkrz1gMXA/8HiBaUwHTgb2zNdDF0I1NHu+hxDuOzy3wMhLhoJPRAQws6HAvu5eZWadgTnAEmCCu083s0ysiU1LDNYl/GpsTWwcMJuQnT+J3bYAZ34Ey1cC/xOnuwJYCawCXgNfAt8dFm4BrMweecIDwGWFZnNtjvk+EbgVGObu1YUGLhU61SkiEvQHqsxsJjAVKAf6Ac/C9mtg2dbCDEJNrNBdCcma2NnA3oQa3z8A/wT853RgD2Ao8C/AX2J5LgnDjxoI6z2cwhwMzI3jvQa2PwhmIyEzz0xMdxUwOv69rhp6nURI3TvN7Huxw33AXsCjZjbTzAYVmJGSoPv4REQAMzsbOMvd/zX+34lQhfq5uz+dqPElhpn8HzDxGzAts73yl9MlhJrYsFwdq4FbwI4BJrn7H7PK9TfwU4C3aXrrUQg3zffQMzxV4xMRAcDdpwLrY63nWUJN6DrgOjObBUyPrS2HxoYgwNfOhA3WzJqYQcW5bK+Jdf2RGd8y++1TZsethk494ZN/gnsXAnU7jrvR6oCpCr1ANT4RkWYw4xFgOE2rSNQBj7pzQSOe85khtMso1IImHz25JUHBJyLSDM17lNjWLdDx/wHH0ajnfNY5mIVnXTeantWZRcEnItJMTXtW55at8K9lcOif4ab+xQ0LhFphoQTU2xny0O0MIiLN5M5ECxFUxNsZOo2GrmVwzV1NnGzySSu5Xn80lfD6I53ezKIan4hIC4kPnL6BcM9CvvfxbQ+kcH3Qh4M19frgVMLd8NkvvL1fDVnyU/CJiLQwM7qR+w3s97uzOrxtoeOp8OmB8HmD7JcirAH+MfH/n4D32PHB00uBEQ5/3QQ+tP42CDP7POFO+U+4e3NufyhZCj4RkVZmZp8EPx+2fQ9OrQy3C+Z7KcKfgVvY8YExENqsVFfD2Qvhz9cmgm9/4CNggbv32lXzsDvTNT4RkWYq9hmf7v66GcdCh8qmv22hM9C5Errs8P4hd/8glqmZc1W6FHwiIs3QxGd8AnQp7hmf+XTs1KwZSCEFn4hI8ySf8QmNe8YnMKWiBd62QLgXUIqh4BMRaZ6FwPQcz/gcDOR5xqedCIf1hnnVUFngdQsNvm2hGtZ+2Lzip4+e1Ski0gxNe8Yn98HKTXB+edOf8bkeOL0cXjqMxNsWzOwzZjYD6G5mM8zsH+PnPzazbrtgEex21KpTRKSNtNRzPlu2VKVPNT4RkbYznvBYsaaoicNLkRR8IiJtxJ0XCecuNxU5aP2Dp/U4siZQ4xYRkTbUtOd86sHTzaFrfCIi7UCxz/ls/RKWDgWfiEg70tBzPtuybKVCwSciIqmixi0iIpIqCj4REUkVBZ+IiKSKgk9ERFJFwSciIqmi4BMRkVRR8ImISKoo+EREJFUUfCIikioKPhERSRUFn4iIpIqCT0REUkXBJyIiqaLgExGRVFHwiYhIqij4REQkVRR8IiKSKgo+ERFJFQWfiIikioJPRERSRcEnIiKpouATEZFUUfCJiEiqKPhERCRVFHwiIpIqCj4REUkVBZ+IiKSKgk9ERFJFwSciIqmi4BMRkVRR8ImISKoo+EREJFUUfCIikiplbV2A3YEZBwBfBo4FugBrgVeAye6sbsuyiYhIcczd27oM7ZYZxwM3AMMAByoTnasBA34PjHfnxdYvoYiIFEvBl4cZVwB3ABUUPiVcB9QAo92Z2BplExGRplPw5ZAIvc5FDLYJhZ+ISLun4MsST2/OpLjQq7cJqHLnpRYtlIiItBi16tzZDYTTm01REYcXEZF2SsGXYNblXjjxfDgpA7fl6WsIMBg4Dngwu2MGJp9r1mmumc02s9+YWfnO07GHzOw5M3vBzC5vyXkQEZHCFHw7+O1aeKEGngMeA5bm6Gcq4UzoH4Abc3Q/dRts+I27nwb8Hfhijp5ucvdTgCrgZjNrag1TRESKVPL38ZnZLKAT8APCvXc/I9yWUAucCXwW6ObuU+DM7qFbBugQf7J1ir8/Ao7O0b1XBdA//rMlTmcH7v56/HMroVWoLrSKiLSSkg8+d68ys87AHGAJMMHdp5tZxt3rgGmJ3ruEX1OAI4GeOca4DTgdWAiMzzfZLmbWFzgbOKVA8W4EHnT3zY2dHxERaZ6SDz4zmxn/LAf6Ac8CxNDLthZmAPcDj+cZYwdgFvABcDxwIbBPVj+LtgCTgRHuXpOnXF8CjgEubuSsiIhICyj54HP3wQBm1gl4gNAy5elEjS9h8ocwsQ6mZXZ8SEu9rYTgywB7EBpxZl+eW10NZx0PfN7dc10kxMyGA5cA5+UJYBER2UVK/j6+WOMc7pWnAAAFVElEQVRz4B3gJuDnhLTaSri+t/0an1nHV6H3UdDNwtB3AIOAa+KgWwgVtA7AZuDq+P8q4Eex/6u2wb3vQd2SWIQp7n5fbL253N2fNrONwGJgY+znUndfvssWgoiIbFfywVcsMx4BhtO0Fq91wKPuXNCypRIRkZai2xl2Np7w7M2mqKFAixcREWl7Cr4s8S0LowmPHytG/bM69bgyEZF2rOQbtzSFOxMtXOXT2xlEREqMrvEVYMZxhGdvnk3+9/FNJbyPTzU9EZHdgIKvEczoRu43sN+vN7CLiOxeFHwiIpIqatwiIiKpouATEZFUUfCJiEiqKPhERCRVFHwiIpIqCj4REUkVBZ+IiKSKgk9ERFJFwSciIqmi4BMRkVRR8ImISKoo+EREJFUUfCIikioKPhERSRUFn4iIpIqCT0REUkXBJyIiqaLgExGRVFHwiYhIqij4REQkVRR8IiKSKgo+ERFJFQWfiIikioJPRERSRcEnIiKpouATEZFUUfCJiEiqKPhERCRVFHwiIpIqCj4REUkVBZ+IiKSKgk9ERFJFwSciIqmi4BMRkVRR8ImISKoo+EREJFUUfCIikioKPhERSRUFn4iIpIqCT0REUkXBJyIiqaLgExGRVFHwiYhIqij4REQkVRR8IiKSKgo+ERFJFQWfiIikioJPRERSRcEnIiKpUtbWBRCR3Z8ZBwBfBo4FugBrgVeAye6sbsuyiWQzd2/rMojIbsqM44EbgGGAA5WJztWAAb8HxrvzYuuXUGRnCj4RaRIzrgDuACoofNmkDqgBRrszsTXKJlKIgk9EipYIvc5FDLYJhZ+0Awo+ESlKPL05k+JCr94moMqdl1q0UCJFUKtOEWk0M/sx9H0KTuoMt+Xp66vAwcCoXB0r4OWxZjbXzDaa2akNTG+WmU1qZrFFdqDgE5EijHgQXq2E54DHgKU5+rkVeDDfCDLQuwrGjgQeLjQlMzsHWN+c0orkouATSTEzGx9rVXPM7Bwz62Fm0+Jnz5hZxsyGmtllYYiHTgM87Do6xJ9shzQw1T0cbhreQLkywDeAu4ueKZEG6D4+kZQys6HAvu5eZWadgTnAEmCCu083s4y71wHTEoMdC1TCFOBIoGdTJl0Zx1NXoJ8vA48QWoOKtCgFn0h69QeqzGxm/L8c6Ac8CxBDL1sXmAHcDzzenGl3Adbk6mBmFcClwFCg4DVAkabQqU6R9FoITHf3we4+mFALWwgMhu2nG7M83gG+S7g8V7lz58ZbW6DbJwjB+ATwQ+AsM8vZUkakKXQ7g0iKmdmtwGcIT115B7gJ+DnhpvStwGfjTzd3n2J24CroegB0szCGO4BBwDVx0G7AzYSHtawC+hIawWwAfhT7X1cNg96BpXsCy4Gp7v5vZnY5sNzdn06UbzDwRXdX8EmLUfCJSKPFZ3K+TQjGpqoBeugZntJWdKpTRBrNnfcI1blCDVMKqQOmKvSkLSn4RKRY42l6a8uaOLxIm1HwiUhR4lsWRhMeP1aM+md16nFl0qZ0O4OIFM2diRaat+jtDLLbUeMWEWkyM44jvI/vbPK/j28q4X18qulJu6DgE5FmM6Mbud/Afr8askh7o+ATEZFUUeMWERFJFQWfiIikioJPRERSRcEnIiKpouATEZFUUfCJiEiqKPhERCRVFHwiIpIqCj4REUkVBZ+IiKSKgk9ERFJFwSciIqmi4BMRkVRR8ImISKoo+EREJFUUfCIikioKPhERSRUFn4iIpIqCT0REUkXBJyIiqaLgExGRVFHwiYhIqij4REQkVRR8IiKSKgo+ERFJFQWfiIikioJPRERS5f8Dru0Az7kha5kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop_chain.projected_graph().draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering that all the methods used before **are chainable** we can also perform the same exact set of operation in one single line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Infos on disease from KEGG:\n",
      "\n",
      "WARNING:root:> File hsa-disease-link already present in C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\n",
      "reading from chached file...\n",
      "INFO:root:Infos on hsa from KEGG:\n",
      "\n",
      "WARNING:root:> File enzyme-hsa-link already present in C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\n",
      "reading from chached file...\n",
      "INFO:root:Infos on enzyme from KEGG:\n",
      "\n",
      "WARNING:root:> File reaction-enzyme-link already present in C:\\Users\\filippo.castelli4\\Documents\\GitHub\\KEGGutils\\tutorials\\kegg_downloads\n",
      "reading from chached file...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kg.KEGGchain(chain = [\"disease\", \"hsa\", \"enzyme\", \"reaction\"]).directed_propagation([\"ds:H00773\"]).projected_graph().draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That concludes this tutorial on `KEGGgraph` and some of it's derived classes, in the next we're gonna see another derived class that's specifically meant for pathways."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
